A310
Course Coordinator:
Erik De Schutter
Computational Neuroscience
Description:
Explore topics in computational neuroscience, from single neuron properties to networks of integrate-and-fire neurons. Review the biophysical properties of neurons and extend these findings to cable theory and passive dendrite simulations. Study excitability based on the Hodgkin-Huxley model of the action potential and the contributions of various other ion channels. Review phase space analysis, reaction-diffusion modeling and simulating calcium dynamics. Model single neurons, neuronal populations, and networks using NEURON software. Discuss seminal papers associated with each topic, and produce reports on modeling exercises.
Aim:
This course introduces basic concepts and methods of computational neuroscience based on theory and a sampling of important scientific papers.
Course Content:
- Introduction and the NEURON simulator
- Basic concepts and the membrane equation
- Linear cable theory
- Passive dendrites
- Modeling exercises 1
- Synapses and passive synaptic integration
- Ion channels and the Hodgkin-Huxley model
- Neuronal excitability and phase space analysis
- Other ion channels
- Modeling exercises 2
- Reaction-diffusion modeling and calcium dynamics
- Nonlinear and adaptive integrate-and-fire neurons
- Neuronal populations and network modeling
- Synaptic plasticity and learning
Course Type:
Elective
Credits:
2
Assessment:
Active participation to textbook discussions in class (40%), reports on modeling papers (40%), written exercises (20%).
Text Book:
- Biophysics of Computation, by Christof Koch (1999) Oxford Press
- Neural Dynamics: From Single Neurons to Networks and Models of Cognition, by Wulram Gerstner, Werner M. Kistler, Richard Naud and Liam Paninski (Cambridge University Press 2014)
Reference Book:
- Computational Modeling Methods for Neuroscientists, edited by Erik De Schutter (MIT Press 2010)
Prior Knowledge:
Requires introductory neuroscience course or equivalent with background knowledge in computational methods, programming, mathematics.
Notes: